In many subfields in neuroscience, neural circuits are thought of as primarily composed of neurons connected by chemical synapses. For example, connectomics is largely focused on locating chemical synaptic connections between neurons; imaging of neural communication through fluorescent tools is also largely focused on chemical synapses, and the vast majority of electrophysiology studies in vitro and in vivo focus on chemical synaptic transmission. However, another class of neural communication mechanism exists that could well govern how neurons work together in intact neural circuits to generate computations: electrical synapses, mediated by gap junction connections that directly electrically couple cells together. Pioneering studies have shown that chronic deletion of gap junction genes can alter hippocampal oscillatory dynamics that may be important for learning and epilepsy, change response of the brain to electrical neuromodulation therapies, alter neural migration and brain development. In astrocytes, gap junctions mediate inter-astrocyte calcium waves and glial communication, a process that might be compromised in psychiatric patients. In cell types outside the brain, genetic deletion of gap junctions can result in inner ear cell loss, and gap junctions are also implicated in retinal function. However, it remains impossible to inactivate gap junction functionality in a transient and reversible fashion, so that their roles ca be analyzed in a time-resolved fashion, i.e. at defined time points in behavioral tasks or during specific neural computation. Accordingly, we propose to develop a fully genetically encoded toolbox for controlling and observing gap junction functionality in defined cell types in neural networks.